The topic of this paper has been motivated by the rising unemployment rate of low-skilled relative to high-skilled labour in Switzerland. Between 1991 and 2014, Switzerland experienced the highest relative increase in the low-skilled unemployment rate among all OECD countries. A natural culprit for this development is “globalization” as indicated by some mass layoffs in Switzerland and as commonly voiced in public debates all over the world. Our analysis, which is based on panel data covering the years 1991 to 2008 and approximately 33,000 individuals employed in the Swiss manufacturing sector, does not, however, confirm this presumption. We do not find strong evidence for a positive relationship between import competition and (low-skilled) individuals’ likelihood of becoming unemployed. Keywords: International trade, Unemployment, Low-skilled labour, Switzerland JEL classification: F14, F16 Introduction between low-skilled labour and high-skilled labour in- The relationship between international trade and employ- creased faster than that of any other OECD country be- ment has always been controversial. Trade economists have tween 1991 and 2014, with virtually no change in the traditionally emphasized the efficiency-enhancing effects of relative wage rate between the same two groups of people. international trade with no impact on total employment, at We use a representative panel data set for employees in least in the medium and long term. Politicians and mem- the Swiss manufacturing sector, covering the period from bers of governments, in contrast, typically believe in an 1991 to 2008, and link it to international trade data. We employment-increasing effect of international trade and control for a number of individual characteristics, particu- often point to the numbers of jobs created by rising ex- larly regarding skills, age and experience, as well as indus- ports. In the eyes of the public, however, international try properties. The analysis indicates that, for the Swiss trade entails the danger of job destruction, particularly economy, rising or high levels of imports do not seem to through increased imports. Trade economists agree that be a driving force behind the probability of becoming un- international trade may have distributional effects within employed. Individual characteristics such as a short length countries. But they typically identify these effects in terms of tenure, part-time employment, and low skills are, how- of changing factor prices: Low-skilled labour may, for ever, confirmed to be important factors that positively example, lose ground—relatively and absolutely—in a high- affect the individual’s risk of becoming unemployed. income country as a result of international trade with (low- Thus, the paper adds to the rapidly expanding literature skilled) labour-abundant countries such as China or India. on whether international trade is an important cause of the In this paper, we investigate whether international increase in the wage and unemployment gaps between trade is indeed linked to the likelihood of becoming skilled and unskilled labour that have been observed in the unemployed. The focus on unemployment is motivated USA and some other countries since the 1980s. We know by our observation that the Swiss unemployment rate since Stolper and Samuelson (1941) and, more generally, since Jones (1965) that trade liberalization tends to have a strong negative impact on some real factor prices and, if * Correspondence: email@example.com Equal contributors these are inflexible or search costs are involved, also on fac- Faculty of Business and Economics, University of Basel, Peter Merian-Weg 6, tor market clearing, as shown by Davis (1998b), Davidson 4002 Basel, Switzerland © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 2 of 12 et al. (1999), and Egger and Kreickemeier (2008). Moreover, discussion” section presents the main results of the Feenstra and Hanson (2003) argue that the effects from econometric analysis. The “Conclusions” section concludes. trade in intermediate inputs may be similar to those caused by skill-biased technological change which is often made Background responsiblefor thewagegap in theUSeconomy.Autor Past research has been motivated by an inquiry into the et al. (2013) found significant negative labour-market effects impact of international trade on relative wages. Feenstra on the US economy of international trade between the (2010, pp. 10), for example, describes and discusses the USA and China and conclude: “Rising imports cause higher development of the wages of “nonproduction” relative to unemployment, lower labor force participation, and re- “production” workers in US manufacturing from 1958 to duced wages in local labor markets that house import- 2006. If we interpret this ratio as the relative wage rate competing manufacturing industries” (p. 2121). of high-skilled to low-skilled labour, the data clearly Recent trade models, which introduce some labour mar- shows that the relative wages of unskilled labour fell ket frictions, as used by Brecher and Chen (2010), Davis and considerably and constantly from 1986 to 2000. This ob- Harrigan (2011), Helpman and Itskhoki (2010), Helpman servation has been the basis for the expanding literature et al. (2010), Larch and Lechthaler (2011), Mitra and Ranjan on trade and the wage gap in the USA that also sparked (2010), or Ranjan (2012), imply that relative unemployment our research interest with its focus on Switzerland. between different types of labour may be affected by trade Such a development is, however, not observable for liberalization in a variety of ways. Moreover, these models Switzerland. Using Swiss labour market panel data come to the conclusion that international trade may also (Swiss Labor Force Statistic, SLFS) and the UNESCO affect the overall unemployment level in an economy—posi- skill classification scheme (International Standard Classifi- 3 7 tively or negatively. In empirical analyses, a negative effect cation of Education, ISCED-97), we calculated both the of trade on overall unemployment is found by Felbermayr median gross wage rate of high-skilled (W ) and low- et al. (2011) and by Gozgor (2014) in cross-country analyses, skilled (W ) labour, and the unemployment rate for the by Hasan et al. (2012) for India and by Francis and Zheng same two groups, i.e. U and U , for the period 1991 to H L (2011) for NAFTA. Chusseau et al. (2010)—in a cross- 2014. Figure 1 shows that, over this period, the U /U L H country analysis—and Horgos (2012)—for Germany—show rose with a compounded annual growth rate (CAGR) of that in the case of inflexible factor prices an increase in the 2%, whilst the W /W remained roughly constant with a H L relative unemployment rate between skilled and unskilled CAGR of − 0.3%. Thus, Fig. 1 serves as a motivation to labour can to some extent be linked to trade—which the study a possible relationship between international trade former call an “inequality-unemployment trade-off”.Fugazza and (changes in) the relative unemployment of low-skilled et al. (2014) find a positive relationship between trade and and high-skilled labour in the Swiss case. unemployment in a panel of 97 countries if countries have A comparison among 21 OECD countries implies that “a comparative advantage in sectors that have high labour there is no other country in which U /U has grown as fast L H market frictions” (p. 1). as in Switzerland from 1991 to 2014. Figure 2 shows a Compared to the existing literature, our empirical investi- CAGR of 4.8% of this ratio from 1991 to 2014 (top panel). gation is of particular interest for three reasons. First, it fo- It reveals that other countries such as South Korea or cuses on a small country whose international trade reflects Germany also experienced a large rise in this ratio, whereas a large share of its domestic output. The Krugman (2000) countries like the Netherlands or Belgium but also the USA critique that a country’s trade volume is typically too small or Canada demonstrateadecreaseofthe relative to explain effects on different types of labour hardly applies in this case (or at least to a much lesser extent). Second, our paper’s emphasis is on the unemployment rate, and not Absolute Value of Relative Unemployment and Relative Wages on wages as underlined by the majority of empirical re- in Switzerland 5 3.5 search studies. This focus is in line with the recent shift in 3.0 research interest among trade theorists and labour-market 2.5 economists as well as with the stylized facts applying to the Swiss economy. Finally, we add to the limited literature on 2.0 Switzerland in this field. The relationship between inter- 1.5 national trade and unemployment has, to our knowledge, 1.0 not been analysed to date for the Swiss case. W /W U /U H L L H The remainder of the paper is as follows. The Fig. 1 Evolution of relative wages and relative unemployment in “Background” section presents stylized facts that explain Switzerland. Source: Own calculations based on FOS (2008), Wyss (2010) our research strategy. The “Methods” section briefly FOS (2016a, b) describes our research methodology. The “Results and 2015 Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 3 of 12 Fig. 2 Averagegrowthrateofrelativeunemployment (toppanel,1991–2014) and absolute value of relative unemployment (bottom panel, 2014) in OECD countries. Note: These are OECD countries for which data were available for the years considered. For the comparison in the top-panel, compounded average growth rates were taken. Source: Own calculations based on OECD (2007) and OECD (2015), Tables A8.4a and A5.4a, respectively unemployment of low-skilled labour. Absolute numbers in whilst more trade leads to some jobs being destroyed in the theOECDdataindicate thatthe Swiss U increased from import-competing sector of an economy, new jobs are sim- 1.2% (1991) to 8.8% (2014), whereas U increased to a ultaneously being generated in the export sector. much smaller extent over this period (from 1.3 to 3.2%). An increase in unemployment is, however, compatible Note, however, that the absolute value of the relative un- with the traditional trade theory if we, for example, ex- employment rate in Switzerland (2.7) is not extremely high, tend a Heckscher-Ohlin type model to allow for some but rather puts the country in the middle of the reported factor price inflexibility as shown by Davis (1998b) or, OECD countries as shown in Fig. 2 (bottom panel). Given adding trade in intermediate inputs, by Egger and the strong and yet unbroken trend in the Swiss relative un- Kreickemeier (2008). The reason is that trade typically employment rate, it is of highest interest to assess whether leads to a decrease in the relative demand for low-skilled trademay beadriving forceofthisdevelopment. labour in a (human) capital-rich country. If the induced fall of the price of low-skilled labour—predicted by the Methods Stolper Samuelson Theorem—is prevented by labour Trade theory stresses the importance of international trade market rigidities, unemployment for low-skilled labour in improving an economy’s allocation of resources, and not tends to rise with trade liberalization. the creation of additional jobs. In a standard trade model, Recent trade models expanded in this direction allowing there is no expected link between trade liberalization and for a number of labour market frictions and/or using intra- the total number of jobs in an economy. The argument industry trade models based on heterogeneous firms and trade economists traditionally have put forward is that job-specific rents. It turns out that, in these set-ups, trade Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 4 of 12 liberalization may indeed raise unemployment of particular Using micro data on individuals’ characteristics, we in- types of labour and affect overall unemployment in an tend to assess whether an individual, who becomes un- economy. In Brecher and Chen (2010), for example, the employed, does so because of his or her particular exposure unemployment rates of low- and high-skilled labour to international trade, controlling—amongst others—for “often move in opposite directions” (p. 990), whereas skills. We present detailed summary statistics of the under- the change of aggregate unemployment is ambiguous. lying data in the “The data” section and then run regres- Davis and Harrigan (2011) argue that, in their model, sions of the change in the individual employment status on trade liberalization may destroy a considerable share of individuals’ characteristics and the trade variables in the highly paid jobs without, however, necessarily affecting “Changes in employment status, individual characteristics overall unemployment. Helpman and Itskhoki (2010, p. and trade” section. The “Refinement of the trade variables 1100) find the surprising result that “[T]he opening to and inclusion of individual fixed effects” section uses a trade raises a country’s rate of unemployment if its relative number of refined trade variables and includes individual labour market frictions in the differentiated sector are fixed effects. The “Sensitivity analyses” section concludes low.” And Hasan et al. (2012, p. 269) come, based on their with some sensitivity analyses. empirical study of India, to the conclusion: “Moreover, our industry-level analysis indicates that workers in indus- The data tries experiencing greater reductions in trade protection For the industry panel data, we rely on the Swiss were less likely to become unemployed, especially in net Labour Force Survey (SLFS). It is based on an annual exporting industries.” and representative collection of information from The focus of our paper is empirical. We seek to ex- Swiss residents (including foreigners, but excluding plain the employment status of individuals over time, i.e. cross-border commuters) by the Swiss Federal Office whether they become unemployed or not, by changes of Statistics (FOS). The SLFS is in line with the and levels of imports and exports, controlling for various methods used by the International Labour Office individual characteristics and industry factors. The ex- (ILO) which defines those individuals as unemployed plained variable (i.e. the individual’s status, y ) is qualita- who are not working, but searching for a job and tive in nature and takes a value of 1 if an individual ready to assume employment quickly. becomes unemployed in a certain year and 0 otherwise. This data source includes a pool of roughly 33,000 The explanatory variables will be qualitative or quantitative individuals over a period of 18 years (1991–2008) who were as will be made more precise in the “Results and discus- employed in the secondary sector (manufacturing) in sion” section. The econometric analysis of the relationship Switzerland. As we want to attribute an industry to an indi- between the two is largely based on the linear probability vidual, characterizing in which kind of industry the worker model (OLS) that includes year and industry fixed effects is employed, we link the SLFS data (FOS, 2009a) on the and, for some specifications, individual fixed effects. We industry two-digit SIC level with the Swiss Foreign Trade use this model as coefficients will be easier to interpret, but Statistics (EZV, 2009) and the National Account Statistics of we also report the results of the analysis based on the logit the FOS (2009b). To also characterize whether an individual model. They turn out to be qualitatively the same. works in a so-called ICT industry (i.e. an industry which displays an above-average intensity in the use of information Results and discussion and communication technology) or in a GAV industry (i.e. We base our analysis on representative industry-panel data an industry which shows an above-average coverage of col- for the years 1991 to 2008. During this period, Switzerland lectively bargained labour contracts), we also take into ac- established a number of bilateral agreements with trading count the ICT-Survey of the KOF Swiss Economic Institute partners—including the European Union (EU). Moreover, (KOF, 2005) at the Swiss Federal Institute of Technology mutual trade liberalization between Switzerland and other (ETH) and the GAV-Statistics of the FOS (2002). countries also occurred through new membership of coun- Summary statistics of the data used in our regressions are tries to the World Trade Organization (WTO), the EU and provided in Table 1. The first column entitled “Change in the European Free Trade Association (EFTA). All of this Employment Status” is composed of individuals who are implies pressure and adjustments that are typical for trade either employed during the full period of observation or liberalizations. The question we now seek to answer is indicate a change in their employment status from employ- whether international trade indeed had a significant impact ment to unemployment. The second column “Employ- on the probability of (particularly low-skilled) individuals ment Status” includes all individuals with a status of to become unemployed. If this is the case, international employed or unemployed. This leads to a maximum of trade could be one reason for the increase of the relative 20,928 (40,875) observations of which 463 (1226) show a unemployment rate for low-skilled labour described in the change in the employment status from employed to “Background” section. unemployed (show a status of unemployment). These Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 5 of 12 Table 1 Summary statistics of the regression data set observations stem from 10,242 (18,995) individuals, of Dependent variable Change in Employment which 461 (1008) show a change in their status from employment status status employed to unemployed (show at least once a status (1) (2) 13 of unemployment). Observations and individuals: Our main econometric analyses will concentrate on the No. of observations 20,928 40,875 observations reported in the first column of Table 1. With status “becoming 463 However, we will take into account the observations in unemployed” the second column in our sensitivity analysis (“Sensitivity With status “being 1226 analyses”). Regarding the first column, the mean year-to- unemployed” year change in percentage of import (export) values in No. of observed individuals 10,242 18,995 the 17 manufacturing industries considered in the ana- Of which becoming 461 (733 obs.) lysis amounts to 6.9% (7.6%). 40.1% of the observa- unemployed at least once tions are linked with “GAV industries”, whereas 37.2% Of which being unemployed 1008 (2838 obs.) at least once of the observations include individuals employed in Mean no. of observations/ 2.0 2.2 “ICT industries”. The distribution of the observed individual worker characteristics are reported in the bottom part Trade covariates: of Table 1 and speak for themselves. Mean annual import 6.9% 6.3% changes Changes in employment status, individual characteristics Median annual import 5.8% 4.6% and trade changes We first regress changes in the individual employment sta- Mean annual export 7.6% 7.1% changes tus on the individuals’ characteristics and aggregate trade Median annual export 6.9% 6.4% variables, using the following linear probability model with changes time and industry fixed effects: Industry characteristics: ICT intensive 37.2% 36.3% y ¼ α þ β ICT þ β GAV þ β SDF þ β IM þ β EX þ ε : i i it it it it it 1 2 3 4 5 Not ICT intensive 62.8% 63.7% ð1Þ GAV sector 40.1% 39.6% Note that i indexes the individual and t the year. The Non-GAV sector 59.9% 60.4% left-hand variable, y , takes the value of 1 if the individual Worker and job characteristics: it i becomes unemployed in t and was employed in t − 1, Mean age 42.6 41.2 and it takes the value of 0 if the individual remains High-skilled 25.2% 23.9% employed in t. The probability of becoming unemployed Medium-skilled 52.1% 52.5% over time is explained based on a number of right-hand Low-skilled 22.8% 23.5% independent variables, starting with an individual being Swiss citizen 60.6% 59.0% employed in an ICT and GAV industry, a number of Foreigner 39.4% 41.0% socio-demographic factors (SDF) of individual i in t as Male 70.4% 69.0% well as imports (IM) and exports (EX)ofthe industry, in Female 29.6% 31.0% which the individual i is employed, in time t. Notethatwe Single 24.4% 27.5% use levels (i.e. the value) as well as changes (i.e. in percent- Married 64.0% 61.2% age) for the trade covariates and also include lags. We also interact some of the variables with the individuals’ skill Widowed 1.7% 1.6% level (L, M, H). The results are provided in Table 2. Divorced 10.0% 9.7% We start with a base regression, leaving out all trade Full-time 86.4% 85.7% variables. The results are reported in the first column of Part-time 13.6% 14.3% Table 2. They show that the likelihood of becoming Fixed contract 98.6% 97.2% unemployed significantly depends on the individual’s Temporary 1.4% 2.8% qualifications (medium and low skills) and type of contract Short tenure (< 1 year) 2.5% 11.3% (part-time, temporary contract). In this respect, we find Medium tenure (1 to < 5 years) 29.3% 29.0% also a positive relationship between the individuals’ likeli- Long tenure (> 5 years) 68.3% 59.7% hood of becoming unemployed and a short or medium Source: Panel data set constructed using data from FOS (2009a), EZV (2009), tenure and for foreigners (typically due to a lack of local KOF (2005) and FOS (2009b). Note that trade covariates and industry language skills). Married and widowed employees, on the characteristics describe the industry which an individual is employed in other hand, are associated with a lower probability of Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 6 of 12 Table 2 Linear regressions of changes in employment status on trade variables and individual characteristics Dependent variable: change in employment status No trade covariates Trade levels Trade levels, lagged Trade first diff. Trade first diff., lagged (1) (2) (3) (4) (5) Trade covariates Imports 0.012 0.010 0.006 0.025 (0.019) (0.019) (0.028) (0.027) Exports − 0.001 − 0.002 0.013 − 0.014 (0.019) (0.019) (0.030) (0.025) Imports*low-skilled 0.017** 0.016** − 0.002 − 0.002 (0.008) (0.007) (0.060) (0.042) Imports*medium-skilled 0.002 0.001 0.007 − 0.027 (0.005) (0.005) (0.036) (0.024) Exports*low-skilled − 0.011* − 0.011* − 0.004 − 0.009 (0.006) (0.006) (0.064) (0.028) Exports*medium-skilled − 0.002 − 0.001 − 0.009 0.012 (0.004) (0.004) (0.029) (0.024) Industry characteristics ICT intensive − 0.005 0.001 0.002 − 0.006 − 0.009 (0.023) (0.026) (0.027) (0.023) (0.012) ICT intensive*low-skilled − 0.001 0.000 0.000 − 0.001 − 0.001 (0.008) (0.008) (0.008) (0.008) (0.008) ICT intensive*medium-skilled − 0.000 0.000 0.000 − 0.000 0.000 (0.005) (0.005) (0.005) (0.004) (0.005) GAV − 0.015 − 0.021 − 0.013 − 0.016 − 0.011 (0.019) (0.044) (0.044) (0.019) (0.017) GAV*low-skilled 0.009 0.012* 0.012* 0.009 0.009 (0.006) (0.007) (0.007) (0.006) (0.006) GAV*medium-skilled 0.002 0.002 0.002 0.001 0.001 (0.005) (0.005) (0.005) (0.005) (0.005) Worker and job characteristics Low-skilled 0.013** − 0.000 0.001 0.013* 0.014** (0.006) (0.006) (0.006) (0.008) (0.007) Medium-skilled 0.006** 0.006 0.007 0.006* 0.007** (0.003) (0.006) (0.006) (0.004) (0.003) Foreigner 0.010** 0.010** 0.010** 0.010** 0.010** (0.004) (0.004) (0.004) (0.004) (0.004) Age − 0.003** − 0.003** − 0.003** − 0.004** − 0.004** (0.001) (0.001) (0.001) (0.001) (0.001) Age^2 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) Female 0.001 0.001 0.001 0.001 0.001 (0.003) (0.003) (0.003) (0.003) (0.004) Married − 0.009*** − 0.009*** − 0.009*** − 0.008*** − 0.008*** (0.003) (0.003) (0.003) (0.003) (0.003) Widowed − 0.019** − 0.019** − 0.019** − 0.018** − 0.018** Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 7 of 12 Table 2 Linear regressions of changes in employment status on trade variables and individual characteristics (Continued) Dependent variable: change in employment status No trade covariates Trade levels Trade levels, lagged Trade first diff. Trade first diff., lagged (1) (2) (3) (4) (5) (0.008) (0.008) (0.008) (0.008) (0.007) Separated 0.007** 0.007** 0.008** 0.008** 0.008** (0.004) (0.004) (0.004) (0.004) (0.004) Part-time worker 0.012** 0.011** 0.011** 0.012** 0.012** (0.005) (0.005) (0.005) (0.005) (0.005) Temporary worker 0.113*** 0.113*** 0.112*** 0.113*** 0.113*** (0.030) (0.030) (0.030) (0.030) (0.030) Short tenure (< 1 year) 0.202*** 0.202*** 0.206*** 0.205*** 0.205*** (0.048) (0.048) (0.049) (0.049) (0.049) Medium tenure (1 to < 5 years) 0.017*** 0.017*** 0.017*** 0.017*** 0.017*** (0.006) (0.006) (0.006) (0.006) (0.006) Constant 0.478* 0.457* 0.496* 0.080** 0.078** (0.267) (0.270) (0.283) (0.039) (0.039) Number of observations 20,928 20,928 20,895 20,878 20,866 Adjusted R 0.086 0.086 0.088 0.089 0.087 Note: All regressions including year and industry fixed effects Source: Panel data set constructed using data from FOS (2009a), EZV (2009), KOF (2005) and FOS (2009b) *p < 0.10, **p < 0.05, ***p < 0.01 becoming unemployed. Note that the coefficient for em- (i.e. changes) in import and export values: A change in im- ployment in an ICT-intensive industry or in a GAV indus- ports or exports in a certain industry does not significantly try is not significantly different from zero. The size of the affect the probability of becoming unemployed. coefficients in Table 2 can be interpreted as follows: Com- We further investigate the impact of trade in the next pared to a high-skilled worker, a low-skilled employee bears subsection by using more refined trade variables and by a 1.3% higher probability of becoming unemployed. including individual fixed effects to take into account Columns (2) to (5) include levels and changes in the any unobserved individual characteristics. trade variables (IM, EX), also interacted with individ- uals’ skill levels (low-skilled, medium-skilled). Trade Refinement of the trade variables and inclusion of levels enter the estimation in logs, whereas “trade first individual fixed effects differences” are calculated as the rate of year-to-year We now regress changes in the individual employment changes in percentage. We also add lagged trade vari- status on a number of trade variables, distinguishing be- ables (lagged by 1 year) to allow for a more deferred ad- tween imports in finished and intermediate products and justment process. Note that, overall, the coefficients of between trade with the North and the South. We elimin- worker and job characteristics do not change in a quali- ate individuals’ characteristics as well as the GAV and ICT tative manner in these different specifications, nor do variables as we now use individual fixed effects. We con- the GAV and ICT coefficients (except for the low skill tinue applying the linear probability model with time fixed level as a consequence of its interaction with the trade effects. Standard errors are clustered by industry. We start variables).Wefindsomeevidence(on the 5% signifi- with taking trade levels (in logs) as explanatory variables cance level) for a significant effect of import levels on and then proceed to look at the rates of changes of the the probability of becoming unemployed for low-skilled same variables. The results are reported in Tables 3 and 4. employees: A 1% higher import value is associated with The estimates reported in Table 3 do not lend broad a 0.017% (0.016% for lagged imports) higher probability support for a positive relationship between the level of of becoming unemployed. In other words, low-skilled imports and the risk of becoming unemployed: Most coef- individuals who work in industries characterized by ficients of the import-level variables are not significantly relatively large contemporaneous imports may, ceteris different from zero. One exception at the 1% significance paribus, face a slightly greater likelihood of becoming level is the coefficient of the 1-year lagged imports of final unemployed. As shown in the fourth and fifth columns of products from the South (fourth column): Individuals Table 2, no significant effects are found for first differences employed in an industry characterized by a 1% higher Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 8 of 12 Table 3 Linear regressions of changes in employment status on Table 4 Linear regressions of changes in employment status on trade levels using individual fixed effects trade differences using individual fixed effects Dependent variable: change in employment status Dependent variable: change in employment status Trade levels Trade levels, lagged Trade first Trade first differences, differences lagged (1) (2) (3) (4) (1) (2) (3) (4) Imports, total 0.016 − 0.002 Imports, total 0.013 − 0.009 (0.022) (0.019) (0.018) (0.015) Exports, total − 0.036* 0.004 Exports, total − 0.025 0.010 (0.018) (0.013) (0.015) (0.007) Imports, final prod., north 0.013 0.003 Imports, final prod., north 0.008 0.002 (0.014) (0.015) (0.014) (0.015) Imports, interm. prod., north − 0.003 0.006* Imports, interm. prod., north − 0.004 − 0.005* (0.005) (0.003) (0.004) (0.003) Imports, final prod., south 0.002 0.008*** Imports, final prod., south − 0.000 0.004** (0.003) (0.003) (0.001) (0.001) Imports, interm. prod., south 0.002 − 0.000 Imports, interm. prod., south 0.002 0.004** (0.006) (0.003) (0.003) (0.001) Exports, final prod., north − 0.002 0.001 Exports, final prod., north − 0.004 − 0.002 (0.012) (0.013) (0.010) (0.013) Exports, interm. prod., north − 0.019* − 0.012 Exports, interm. prod., north − 0.001 − 0.010 (0.009) (0.009) (0.005) (0.008) Exports, final prod., south − 0.009 0.001 Exports, final prod., south − 0.002 0.004 (0.008) (0.006) (0.003) (0.004) Exports, interm. prod., south 0.010** − 0.001 Exports, interm. prod., south 0.004*** 0.007** (0.004) (0.004) (0.001) (0.003) Constant 0.149*** 0.118*** 0.100** 0.102** Constant 0.104*** 0.104*** 0.103*** 0.101*** (0.044) (0.028) (0.043) (0.045) (0.013) (0.014) (0.014) (0.014) Number of observations 20,928 19,438 20,895 19,406 Number of observations 20,878 19,391 20,866 19,380 Adjusted R 0.045 0.047 0.045 0.047 Adjusted R 0.045 0.045 0.045 0.048 Note: All regressions including time and individual fixed effects Source: Panel data set constructed using data from FOS (2009a), EZV (2009), Note: All regressions including time and individual fixed effects KOF (2005) and FOS (2009b) Source: Panel data set constructed using data from FOS (2009a), EZV (2009), *p < 0.10, **p < 0.05, ***p < 0.01 KOF (2005) and FOS (2009b) *p < 0.10, **p < 0.05, ***p < 0.01 value of imports in this category encounter a 0.008% higher probability of becoming unemployed. (2) and (4)—0.004 and 0.007—are significantly different The results of the analogous estimations for first dif- from zero (and positive) should not be overvalued. ferences (i.e. rates of changes) in the import and ex- port variables in a given industry are reported in Sensitivity analyses Table 4. We neither find an unambiguous relationship We finally try a number of different specifications to test between changes in imports and the risk of unemploy- the robustness of our results. Detailed results of these ment nor is any of the relationship significant on the analyses are available from the Additional file 1 to this 1% level. However, we find that the coefficients for a paper (Tables OA1 to OA5). lagged increase in final as well as intermediate imports First, we replicate the results presented in Tables 2, 3 from the South are significantly different from zero and 4 using the logit regression model (Additional file 1: (on the 5% level, fourth column). Note that the eco- Tables OA2 and OA3). Regarding the results in Table 2, nomic impact of this effect is small: A 1% increase in the logit estimates confirm a relationship between import import value, denoted as 0.01 in the dataset, leads to an levels and the likelihood of low-skilled workers of becom- increase in the probability of becoming unemployed by ing unemployed: Coefficients are significantly different 0.004%. On this background, the fact that the coefficients from zero (at the 5% level) with a positive sign. Also, we of intermediate export products to the South in columns can confirm sign and significance level for the individual Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 9 of 12 socio-demographic variables included and reported in skilled labour. The striking increase in the Swiss un- Table 2. Using a logit model with fixed effects, analogously employment rate of low-skilled relative to high-skilled to Tables 3 and 4, we do not find any significant effects of labour from 1991 to 2014—with virtually no changes of the trade variables, regardless of whether we use levels or relative wages—motivated us to focus our research on the first differences as explanatory variables. Hence, the logit relationship between international trade and unemploy- estimations lead to qualitatively identical results as the lin- ment for Switzerland. ear regression model. Our assessment of the Swiss case does not confirm the Second, we use the employment status (i.e. the informa- public concerns. The econometric analysis of a data set of tion whether an individual is employed (0) or unemployed roughly 30,000 workers in the Swiss manufacturing sector (1) in period t)—instead of the change of the employment from 1991 to 2008, which we link with the Swiss foreign status—as the dependent variable (summary statistics can trade statistics, does not, overall, support the presumption be found in the second column of Table 1). As a start, we that an increase in imports has a statistically significant replicate the estimations described in Table 2 with the (and positive) effect on the probability of individuals of be- new dependent variable (see Additional file 1: Table OA4). coming unemployed, irrespective of their skills. Thus, we Again, we can confirm positive coefficients regarding im- seem to be left with other well-established factors such as port levels interacted with low-skilled labour for lagged the level of skills, temporary employment or the length of imports (significantly different from zero at the 5% level). tenure to explain the individuals’ risk of unemployment. Furthermore, we use trade levels and first differences as The startling rise in the relative unemployment rate of explanatory variables in a model with individual fixed ef- low-skilled labour and, at the same time, the somewhat fects and find results that are qualitatively similar to those comforting constant relative wage rate of low-skilled in Tables 3 and 4. The results for the employment status labour in Switzerland from 1991 to 2014 still remains to as the dependent variable are reported in Additional file 1: be explained. Obvious candidates to look at more carefully Table OA5. Most coefficients are not significantly different would, in our view, be a skill-biased technological change from zero. One exception is, again, the lagged level of final for the relative unemployment rate and the compositional imports from the South with a coefficient of 0.016 (signifi- change in immigration for the relative wage rate. cantly different from zero at the 1% level). However, we Our investigation therefore only offers an initial basis for also find a negative coefficient for the lagged first differ- amoreprofoundanalysisofthe labour market effects of ences of intermediate imports from the North (− 0.010, trade or, more generally, of globalization for Switzerland. significantly different from zero at the 5% level), leaving us First, the fact that we find a weak (albeit small) positive with an ambiguous result regarding the effect of imports relationship between low-skilled individuals working in in- on the status of employment. dustries characterized by a relatively high level of imports Third, and complementary to the analyses in Tables 3 (particularly from the South) and the probability of their and 4 (with again the change of the employment status as becoming unemployed may indicate something that we are the dependent variable), we use second differences of the not able to identify, given the limited statistical power of trade variables (e.g. [IM − (IM )/(IM )]) instead of first our data set which includes only a relatively small number t t − 2 t − 2 differences and 2-year lags of trade levels instead of 1-year of individuals who became unemployed. Second, we use ex- lags. All the results including the ones from Tables 3 and 4 ports as a control variable for (changes in) demand, because are reported in Additional file 1: Table OA1. We find a increasing imports have different effects on employment if negative coefficient for the second differences without lags they are combined with rising exports. This presents no of intermediate imports from the North (− 0.006, signifi- problem as long as the domestic markets remain rela- cantly different from zero on the 5% level) in column 14. tively small, which may, even in a small country such Furthermore, a positive coefficient is found for intermediate as Switzerland, not always be the case. If compatible import levels from the North lagged by 2 years (0.013, sig- data were available, a more sophisticated ratio could nificantly different from zero on the 5% level) in column 6. be used such as the import penetration ratio pro- All the other import coefficients are insignificantly different posed by Autor et al. (2014) for the US industries. from zero. Thus, also in these regressions, we do not find Third, the fact that the individuals’ characteristics could unambiguous evidence for a positive relationship between only be linked to the two-digit SIC industry level, may even imports and the probability of becoming unemployed. out a large amount of variation within industries: An indi- vidual’s employment status may be affected by imports on Conclusions a sub-industry level, which might remain unobserved on This paper has been sparked by the omnipresent public the aggregated industry level. Also, and related to this, concern in many industrial countries that international individuals employed in large multiproduct firms may be trade through specialization and outsourcing may cause linked to an industry which is not really relevant to their income losses and unemployment, particularly for low- actual occupation. Thus, an analysis based on more Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 10 of 12 disaggregated, possibly even firm- or establishment-level, (ISCED 3-4: professional education which, most import- data may challenge our results. antly, includes completed apprenticeships). On the other hand, this paper’s lack of findings in support Note that U is defined as the unemployment rate of of a strong positive relationship between import competi- the 25–64-year-olds with “below upper secondary edu- tion and the risk of unemployment could also be a conse- cation”,whereas U is defined as the unemployment quence of the relatively low unemployment rate in rate of the 25–64-year-olds with “tertiary education”; Switzerland and the alleged high degree of flexibility in the see OECD (2007, 2015). Swiss labour market. If individuals lose their job because of Interestingly, South Korea shows the lowest absolute rate import competition, but immediately find a new one, they of relative unemployment in 2014 despite the considerable never become unemployed. In this regard, it is interesting increase reported in Fig. 2. On the other extreme, the Czech to note that our analysis of six announced mass-layoff cases Republic shows a fall of the relative unemployment rate in Swiss manufacturing due to globalization between 2001 from 1991 to 2014, but remains the country with the highest and 2006 revealed exactly this situation: Only one quarter ratio in 2014; note that, in 2014, U (U ) equaled 20.7% L H of the displaced workers were, in the end, dismissed by (2.6%) for this country (see OECD (2015, Table A5.4a)). their companies and thus became, at least for a short term, Baldwin (1994, p. 73) once called the view that trade unemployed (see Wyss, 2010). The others swiftly found a affects the number of jobs as “utter nonsense from the new job in the same or in another company or industry. medium- or long-run economic perspectives”. Davidson et al. (1999) would, however, add that in a trade model with labour market frictions this is, in principle, possible, Endnotes and mainly an empirical question (p. 273). 1 11 Interestingly, this point of view is emphasized, for ex- Dutt et al., 2009, p. 33) emphasize a “fairly strong and ample, in an early document of the U.S. Department of robust empirical support (…) for the Ricardian prediction State (1945) that formed the basis of the creation of the that trade openness and unemployment are negatively re- General Agreement on Tariffs and Trade (GATT). The lated across all countries”. The intuition is that trade raises title “Proposals of Expansion of World Trade and Em- productivity which increases the search effort of employees ployment” is revealing. and employers that, in turn, reduces unemployment. 2 12 For early contributions see, for example, Berman Note that, during this period, Switzerland or EFTA (to et al. (1994), Borjas et al. (1991), Davis (1998a, 1998b), whom Switzerland belongs) established free trade agree- Feenstra (1998, 2010), Krugman (1995, 2000), Lawrence ments with approximately 20 countries (e.g. with Turkey and Slaughter (1993), Leamer (1998, 2000) or Murphy (1992), Mexico (2001), South Korea (2006) and China and Welch (1991). (2014)), reached two bilateral agreements with the EU Whereas the overall effect on unemployment remains (1999, 2004) and was—through its free trade agreement ambiguous or depends on parameters in these models, (1972) and the two bilateral agreements with the EU—also Dutt et al. (2009) predict a reduction in overall un- affected by the enlargement of the EU by 13 new member employment as a result of trade. countries in 2004, 2007 and 2013. Finally, there are Moser et al. (2011) find a small (negative) effect from approximately 30 countries (including China in 2001) a reduction in the international competitiveness of firms that became additional members of the WTO, after its on job flows for Germany, and more so on job creation foundation in 1995 until 2008, and thus achieved im- rather than on job destruction. proved mutual market access with Switzerland. 5 13 See for example Feenstra and Hanson (1999), Hijzen The deviation to all 33,000 individuals mentioned et al. (2005) and OECD (2007) for a broad overview. above is due to the fact that many individuals exhibit See Suarez (1998) and Müller, Marti and Nieuwkoop missing values in at least one of the variables of interest. (2002) who focus on trade and wages. Other studies such as SeeAppendix: Table5regardingthe assignment of indi- Sheldon (2007), Puhani (2003) and Arvanitis (2005) analyze vidual industries. GAV stands for “Gesamt-Arbeits-Vertrag” shifts in supply and demand on the Swiss labour market, and means collective bargaining contract; ICT stands for but do not explicitly investigate the effects of trade. “Information and Communication Technology”. 7 15 Note that, throughout this paper, high-skilled (H) is de- Here and in the following we consider coefficients as fined as people with tertiary education (ISCED 5-6: univer- significantly different from zero if they reach at least the sity, college of higher education (Fachhochschule) and 5% level. school of higher education (Höhere Fachschule). Low- Note that Feenstra and Hanson (2003) also base their skilled (L) is defined as individuals with primary or lower analysis on annual changes broken down to final and inter- secondary education (ISCED 1-2: mandatory education with mediate imports. Anderton and Brenton (1999) differentiate no professional training qualification). Medium-skilled (M) between imports from industrial and low-wage countries. is defined as individuals with upper secondary education Based on the Swiss Trade Statistics, intermediates are Mohler et al. Swiss Journal of Economics and Statistics (2018) 154:10 Page 11 of 12 defined as items in the category “raw materials”, “semi-fin- Additional file ished products” and “intermediate goods”.Analternative Additional file 1: Table OA1. Linear regressions of changes definition based on input-output tables is currently not feas- in employment status on trade variables using individual fixed effects. ible as relevant statistics are not available. We also distin- Table OA2. Logit regressions of changes in employment status on trade guish between imports from the North (industrial countries) variables and individual characteristics, regression coefficients. Table OA3. Logit regressions of changes in employment status on trade variables using and the South (developing countries). individual fixed effects, regression coefficients. Table OA4. Linear regressions Individuals remain in the same industry throughout of employment status on trade variables and individual characteristics, the observed period. Hence, ICT and GAV variables are regression coefficients. Table OA5. Linear regressions of employment status on trade variables using individual fixed effects, regression coefficients. omitted when using individual fixed effects. (DOCX 54 kb) One may observe that the logit analysis implies a positive relationship (significantly different from zero at the 5% level) between low-skilled individuals working in Acknowledgements All persons who provided feedback as well as some minor support (data, GAV-industries (interacted variable) and their probabil- editorial) to the different versions of the paper are mentioned in the ity of becoming unemployed. acknowledgement. Also the relationship between the employment status We would like to thank the co-editor, Volker Grossmann, and two anonymous referees for their extremely helpful suggestions which led to a considerable and exports (level, change) remains ambiguous in the improvement of the analysis in our paper. We also thank Marius Brülhart, David analysis. Green, Douglas A. Irwin, Ronald W. Jones, Peter Kugler, Christian Rutzer and Note that we also use 1-year leads of the trade vari- George Sheldon for helpful feedback to earlier drafts as well as Dragan Filimonovic, Lukas Hohl and Hermione Miller-Moser for data and editorial ables as “placebo tests”. We refrain from showing those support. We also benefited from discussions at the Annual Conference of the results in the Additional file 1 as we do not find any sig- European Trade Study Group (ETSG), the Annual Meeting of the Swiss Society nificant results. of Economics and Statistics and a lunch seminar at the Department of Economics of the University of British Columbia (UBC). Simone Wyss gratefully For analyses of skill-biased technological change, see acknowledges financial support from the WWZ-Forum and the State Secretariat the seminal contributions by Berman et al. (1994, 1998) for Economic Affairs (SECO) during an early stage of the research project. and Krugman (2000) as well as, for an attempt to disen- tangle trade and technology effects, Autor et al. (2015). Authors’ contributions LM has implemented all the regressions in the second and the final version of the paper and given input to the first and second revisions of the paper. He Appendix also contributed to the letters to the editor and the referees. RW has written the first version of the paper and re-written the paper as part of the first and Table 5 Industry dummies for ICT intensity and GAV intensity second revisions. He also wrote the letters to the editor and the referees. RW and LM have been closely working together in the first and second revisions Industry ICT GAV intensive intensive of the paper. SM has collected the data and implemented the econometric analysis for the first version of the paper. She also answered questions regarding 1 Mining and quarrying 0 0 the data and the original regressions throughout the revision process. All 2 Manufacture of food products and beverages 0 0 authors read and approved the final manuscript. 3 Manufacture of textiles 0 0 4 Manufacture of wearing apparel 1 0 Competing interests The authors declare that they have no competing interests 5 Manufacture of wood and of products of wood 0 1 6 Manufacture of paper and paper products 0 0 7 Publishing, printing and reproduction of recorded 10 Publisher’sNote media Springer Nature remains neutral with regard to jurisdictional claims in 8 Manufacture of chemicals and chemical products 0 0 published maps and institutional affiliations. 9 Manufacture of rubber and plastics products and 00 Received: 15 May 2017 Accepted: 10 December 2017 other non-metallic mineral products 10 Manufacture of basic metals 0 1 11 Manufacture of fabricated metal products 0 0 References 12 Manufacture of machinery and equipment 1 1 Anderton, B, & Brenton, P. (1999). Outsourcing and low-skilled workers in the UK. 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Published: Jun 5, 2018